Density Functional Theory Optimization Research Articles

In-silico design of Metal oxide-Biochar composites for enhanced dibenzothiophene adsorption: DFT and statistical physics approach

This study proposes an efficient in-silico adsorbent material selection strategy for clean fuel combustion by leveraging Density Functional Theory (DFT) calculated reactivity parameters to optimize material configurations for enhanced dibenzothiophene (DBT) adsorption from model fuel oil (n-octane). A DFT screening identified TiO2 as the optimal material for DBT adsorption from biochar and four metal oxide (MOx) based on calculated adsorption energies (Eads). Further DFT optimization identified a 10 % TiO2 composition with biochar (TBC10) as the optimal material. This composite exhibited the strongest DBT interaction (−370.3 kcal mol−1) and a remarkably short sulfur-titanium bond (2.64 Å), indicative of an ideal adsorption geometry. Notably, TBC10 features a unique composite structure: TiO2 connected to BC via van der Waals forces, while DBT directly interacts with AC through π-π stacking and covalent sulfur-titanium bonds. To validate theoretical predictions, the designed composites were synthesized and experimentally evaluated. The excellent alignment between experimental adsorption performance and DFT-calculated adsorption energies corroborated TBC10′s superior DBT adsorption capacity. Employing a systematic optimization process, the most effective parameters for TBC10′s DBT removal from a 150 mL solution containing an initial DBT concentration of 45 mg L-1 were established as: a 30 min contact time, an adsorbent dosage of 13 mg, and a temperature of 25 °C. Under these conditions, TBC10 achieved a remarkable removal efficiency (RRDBT) of 99.82 % and a maximum DBT uptake capacity (qDBT) of 31.1 mg g−1. Kinetic and statistical physics modeling revealed a multi-molecular adsorption process with distinct binding sites, characterized by spontaneous endothermic interactions and involving both physical and chemical mechanisms. The in-silico methodology for adsorbent design reduces experimentation time, cost and identifies optimal candidates for DBT removal, enhancing fuel desulfurization and promoting sustainable combustion. This novel in-silico design paradigm holds immense potential for the development of next-generation adsorbents for various environmental remediation and industrial separation processes.

Accurate Prediction of NMR Chemical Shifts: Integrating DFT Calculations with Three-Dimensional Graph Neural Networks.

Computer prediction of NMR chemical shifts plays an increasingly important role in molecular structure assignment and elucidation for organic molecule studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) have established a framework to predict NMR chemical shifts but often at a significant computational expense with a limited prediction accuracy. Recent advancements in deep learning methods, especially graph neural networks (GNNs), have shown promise in improving the accuracy of predicting experimental chemical shifts, either by using 2D molecular topological features or 3D conformational representation. This study presents a new 3D GNN model to predict 1H and 13C chemical shifts, CSTShift, that combines atomic features with DFT-calculated shielding tensor descriptors, capturing both isotropic and anisotropic shielding effects. Utilizing the NMRShiftDB2 data set and conducting DFT optimization and GIAO calculations at the B3LYP/6-31G(d) level, we prepared the NMRShiftDB2-DFT data set of high-quality 3D structures and shielding tensors with corresponding experimentally measured 1H and 13C chemical shifts. The developed CSTShift models achieve the state-of-the-art prediction performance on both the NMRShiftDB2-DFT test data set and external CHESHIRE data set. Further case studies on identifying correct structures from two groups of constitutional isomers show its capability for structure assignment and elucidation. The source code and data are accessible at https://yzhang.hpc.nyu.edu/IMA.

Toward Atomistic Understanding of Iron Phosphate Glass: A First-Principles-Based Density Functional Theory Modeling and Study of Its Physical Properties.

Iron phosphate glasses (IPGs) have been proposed as futuristic materials for nuclear waste immobilization and anode materials for lithium batteries. Recently, many attempts have been made to propose atomistic models of IPGs to explain their properties from an atomistic viewpoint. In this paper, we seek to produce small scale models of IPG that can be handled within the scheme of density functional theory (DFT) to study its electronic structure. The starting models, generated using the Monte Carlo (MC) method, were subsequently annealed using ab initio molecular dynamics (AIMD) to minimize the coordination defects. The geometry of the equilibrated structure was then optimized at 0 K. This hybrid approach (MC + AIMD + DFT optimization) produced good atomistic models of IPG, which can reproduce the experimentally observed band gap, vibrational density of states, magnetic moment of Fe, and mechanical and optical properties. Computationally expensive melt-quench simulation can be avoided using the present approach, allowing the use of DFT for accurate calculations of properties.

Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts.

This work aims to address the challenge of developing interpretable ML-based models when access to large-scale computational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor-based approaches, machine learning-based force fields, and low-cost density functional theory (DFT) calculations to predict high-quality adsorption energies for H, N, and NHx (x = 1, 2, and 3) adsorbates. This is achieved using three specific modifications to typical DFT workflows including: (1) using a sequential optimization protocol, (2) developing a new geometry-based descriptor, and (3) repurposing the already-available low-cost DFT optimization trajectories to develop a ML-FF. Taken together, this study illustrates how cost-effective DFT calculations and appropriately designed descriptors can be used to develop cheap but useful models for predicting high-quality adsorption energies at significantly lower computational costs. We anticipate that this resource-efficient philosophy may be broadly relevant to the larger surface catalysis community.

Antifungal potential of mannopyranoside derivatives through computational and molecular docking studies against Candida albicans 1IYL and 1AI9 proteins

Methyl α-D-mannopyranoside (MAM) is a naturally occurring carbohydrate derivative that has gained attention in drug discovery due to its potential therapeutic applications, particularly as an antifungal agent. In this study, we employed a computational approach to investigate the interactions between MAM and two Candida albicans antifungal proteins, 1IYL and 1AI9, through molecular docking simulations. Furthermore, we performed a PASS (Prediction of Activity Spectra for Substances) analysis to predict MAM potential biological activities, explored the pharmacokinetic properties and ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles, and optimized the MAM using the density functional theory (DFT) method. The molecular docking results revealed favorable binding interactions between MAM and the active sites of the 1IYL and 1AI9 proteins, suggesting potential antifungal activity. Additionally, the ADMET profiles indicated low toxicity and suitable drug-like properties, such as moderate metabolic stability and minimal risk of adverse effects. Furthermore, DFT optimization was performed to investigate the molecular geometry and electronic properties of MAM. The optimization results provided valuable information on the stability and reactivity of MAM, enabling a better understanding of its chemical behavior and potential modifications for enhanced activity. Finally, PASS prediction was employed to evaluate MAM's potential biological activities beyond its antifungal properties. The analysis revealed several potential activities, including antibacterial, antiviral, and immunomodulatory effects, expanding the scope for future research and therapeutic applications. In conclusion, this computational study sheds light on the molecular interactions, pharmacokinetic properties, ADMET profiles, DFT optimization, and PASSES predictions of MAM. These findings highlight the potential of MAM as a promising antifungal agent with favorable pharmacological properties.

NICE-FF: A non-empirical, intermolecular, consistent, and extensible force field for nucleic acids and beyond.

A new non-empirical abinitio intermolecular force field (NICE-FF in buffered 14-7 potential form) has been developed for nucleic acids and beyond based on the dimer interaction energies (IEs) calculated at the spin component scaled-MI-second order Møller-Plesset perturbation theory. A fully automatic framework has been implemented for this purpose, capable of generating well-polished computational grids, performing the necessary abinitio calculations, conducting machine learning (ML) assisted force field (FF) parametrization, and extending existing FF parameters by incorporating new atom types. For the ML-assisted parametrization of NICE-FF, interaction energies of ∼18 000 dimer geometries (with IE < 0) were used, and the best fit gave a mean square deviation of about 0.46 kcal/mol. During this parametrization, atom types apparent in four deoxyribonucleic acid (DNA) bases have been first trained using the generated DNA base datasets. Both uracil and hypoxanthine, which contain the same atom types found in DNA bases, have been considered as test molecules. Three new atom types have been added to the DNA atom types by using IE datasets of both pyrazinamide and 9-methylhypoxanthine. Finally, the last test molecule, theophylline, has been selected, which contains already-fitted atom-type parameters. The performance of NICE-FF has been investigated on the S22 dataset, and it has been found that NICE-FF outperforms the well-known FFs by generating the most consistent IEs with the high-level abinitio ones. Moreover, NICE-FF has been integrated into our in-house developed crystal structure prediction (CSP) tool [called FFCASP (Fast and Flexible CrystAl Structure Predictor)], aiming to find the experimental crystal structures of all considered molecules. CSPs, which were performed up to 4 formula units (Z), resulted in NICE-FF being able to locate almost all the known experimental crystal structures with sufficiently low RMSD20 values to provide good starting points for density functional theory optimizations.

Density Functional Theory Optimization of Cobalt- and Nitrogen-Doped Graphene Catalysts for Enhanced Oxygen Evolution Reaction

The optimization and advancement of effective catalysts in the oxygen evolution reaction (OER) are integral to the evolution of diverse green power technologies. In this study, cobalt–nitrogen–graphene (Co-N-g) catalysts are analyzed for their OER contribution via density functional theory (DFT). The influence of vacancies and nitrogen doping on catalyst performance was probed via electronic features and related Frontier Molecular Orbitals. The research reveals that the double-vacancy nitrogen-doped catalyst (DV-N4) exhibits remarkable OER effectiveness, characterized by a notably low overpotential of 0.61 V. This is primarily attributed to enhanced metal–ligand bonding interactions, a diminished energy gap indicating augmented reactivity, and advantageous charge redistribution upon water adsorption. Additionally, nitrogen doping is found to facilitate electron loss from Co, thus promoting water oxidation and improving OER performance. This research provides crucial insights into high-performance OER catalyst design, informing future developments in efficient renewable energy devices.

Distyrylbenzene-based semi-conducting molecular material for OLED applications: Synthesis, characterization and TD-DFT calculations

New semi-conducting molecular materiel (M-DSB) based on the distyrylbenzene π-conjugated system was synthesized via the Wittig reaction. The structural properties of M-DSB, displaying three different stereoisomers E-E, Z-Z and Z-E, were investigated through density functional theory (DFT) optimization. The detailed TD-DFT analysis provided insights into the structure-property relationships and revealed good agreement with experimental data. This photoactive material exhibits nearly the same UV–visible spectra in dilute solution and as solid thin film, with a maximum absorption at 363 nm and an optical gap of 2.99 eV. A broad and red-shifted photoluminescence spectrum was obtained in the solid state with blue emission compared to the solution which shows purplish blue emission with relatively narrow emission. The HOMO/LUMO energy levels were calculated by cyclic voltammetry measurements and indicate a p-type semiconductor material. The temperature and frequency dependence of the conductivity shows an activation energy value of about 0.89 eV for the M-DSB thin layer. Impedance spectroscopy of the ITO/M-DSB/Al device indicates a typical behavior of a single relaxation process and confirms that the electrical response becomes faster and the mobility of the charge carriers is enhanced by increasing the temperature.

Unlocking the secrets of doxepin hydrochloride's crystal structure: Insights from high-quality powder diffraction analysis

Doxepin hydrochloride, a versatile pharmaceutical compound, has been the subject of extensive research aimed at elucidating its crystal structure and solid-state characteristics. In this manuscript, we explore the significance of high-quality powder diffraction data in unveiling the intricate details of doxepin hydrochloride's crystal lattice. By examining the refined atom coordinates, density functional theory (DFT) optimization, and intermolecular interactions, we gain valuable insights into its structural conformation. This knowledge highlights the importance of precise crystallographic data in advancing our understanding of complex compounds and their pharmaceutical applications.

Benzenesulfonamide based 1,3,4-oxadiazole derivatives: synthesis, pharmacokinetic property prediction, bovine carbonic anhydrase activity and molecular docking studies

Sulphur-containing compounds are highly significant as they can possess a variety of biological activities that make them useful for pharmacological purposes and for the mechanism by which drugs such as antibiotics bind to and disrupt bacterial cell walls. In this study, novel thioalkyl substituted-1,3,4 oxadiazole-bearing sulfonamide compounds have been successfully synthesized and characterized by 1HNMR, 13CNMR, IR and elemental analysis. The effects of different thioalkyl groups on the 1,3,4 oxadiazole group, the IC50 value for Bovine Carbonic Anhydrase (BCA) found by in vitro, density functional theory (DFT) calculations, pharmacokinetics prediction and molecular docking are aimed to reveal the interactions on BCA. Firstly, pharmacokinetic predictions of thioalkyl substituted 1,3,4-oxadiazole compounds were generated to predict their potential hazards. Secondly, the predicted molecular docking data and 2D interaction were analyzed based on the best configuration from DFT optimization. Finally, the inhibition against BCA was analyzed in vitro and compared with the theoretical data. The compound (5o) has the best value such as IC50 = 51.80 µM, HOMO–LUMO (ΔE 4.488 Ev), ΔG −7.69 kcal/mol, Full fitness −2152.72 FF and predicted toxicity results showed no significant results except hepatotoxicity.

Simulating excited-state complex ensembles: Fluorescence and solvatochromism in amine-arene exciplexes.

Exciplexes are excited-state complexes formed as a result of partial charge transfer from the donor to the acceptor species when one moiety of the donor-acceptor pair is electronically excited. The arene-amine exciplex formed between oligo-(p-phenylene) (OPP) and triethylamine (TEA) is of interest in the catalytic photoreduction of CO2 because it can compete with complete electron transfer to the OPP catalyst. Therefore, formation of the exciplex can hinder the generation of a radical anion OPP·- necessary for subsequent CO2 reduction. We report an implementation of a workflow automating quantum-chemistry calculations that generate and characterize an ensemble of structures to represent this exciplex state. We use FireWorks, Pymatgen, and Custodian Python packages for high-throughput ensemble generation. The workflow includes time-dependent density functional theory optimization, verification of excited-state minima, and exciplex characterization with natural transition orbitals, exciton analysis, excited-state Mulliken charges, and energy decomposition analysis. Fluorescence spectra computed for these ensembles using Boltzmann-weighted contributions of each structure agree better with experiment than our previous calculations based on a single representative exciplex structure [Kron et al., J. Phys. Chem. A 126, 2319-2329 (2022)]. The ensemble description of the exciplex state also reproduces an experimentally observed red shift of the emission spectrum of [OPP-4-TEA]* relative to [OPP-3-TEA]*. The workflow developed here streamlines otherwise labor-intensive calculations that would require significant user involvement and intervention.

Amazon raw clay as a precursor of a clay-based adsorbent: experimental study and DFT analysis for the adsorption of Basic Yellow 2 dye.

A clay-based adsorbent (CBA) was purified from a sustainable precursor (raw clay, RC), which was obtained from the Amazon region in Brazil. The CBA was characterized using X-ray diffraction (XRD), Fourier Transform Infrared spectroscopy (FTIR), Brunauer-Emmet-Teller surface area (SBET, RC = 23.386 m2.g-1, CBA = 33.020 m2.g-1), Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), thermogravimetric analysis (TGA), cation exchange capacity (CEC, CBA = 44.75 cmol/kg), and point of zero charge analyses (pHPZC, CBA = 2.20). Subsequently, CBA was used to adsorb basic yellow 2 (BY2) dye from aqueous solutions. A CBA dosage (1g/L), initial concentration of dye (C0 = 15mg/L), and pH (5.6) were ideal conditions for the BY2 dye removal of ~ 98%. The BY2 kinetics was better represented by the pseudo-first-order (PFO) model while the BY2 equilibrium was well represented by the Sips model, with a maximum adsorption capacity of qms = 18.04mg/g at 28°C. The negative values of ΔG° and ΔH° showed that the studied process is spontaneous and exothermic, while the values of isosteric heat (∆Hst, -16 to -20kJ/mol) suggest a predominance of physical interactions. The molecular chemical reactivity of BY2 was investigated using quantum chemical descriptors calculated based on Density Functional Theory (DFT) optimization of the dye molecule, and the results revealed a large energy gap value (4.3900eV) and considerable chemical hardness (η = 2.1950eV). Therefore, the correlation between DFT and experimental results consistently sustains that BY2 dye tends to be adsorbed on the CBA surface by electrostatic interactions, thus, this is the possible adsorption mechanism of this process.

Formation and fragmentation of 2-hydroxyethylhydrazinium nitrate (HEHN) cluster ions: a combined electrospray ionization mass spectrometry, molecular dynamics and reaction potential surface study.

The 2-hydroxyethylhydrazinium nitrate ([HOCH2CH2NH2NH2]+NO3-, HEHN) ionic liquid has the potential to power both electric and chemical thrusters and provide a wider range of specific impulse needs. To characterize its capabilities as an electrospray propellant, we report the formation of HEHN cluster ions in positive electrospray ionization (ESI) and their collision-induced dissociation. The experiment was carried out using ESI guided-ion beam mass spectrometry which mimics an electrospray thruster in terms of ion emission, injection into a vacuum and fragmentation in space. Measurements include compositions of primary ions in the electrospray plume and their individual dissociation product ion cross sections and threshold energies. The results were interpreted in light of theoretical modeling. To determine cluster structures that are comprised of [HE + H]+ and NO3- constituents, classical mechanics simulations were used to create initial guesses; and for clusters that are formed by reactions between ionic constituents, quasi-classical direct dynamics trajectory simulations were used to mimic covalent bond formation and structures. All candidate structures were subject to density functional theory optimization, from which global minimum structures were identified and used for construction of reaction potential energy surface. The comparison between experimental values and calculated dissociation thermodynamics was used to verify the structures for the emitted species [(HEHN)nHE + H]+, [(HEHN)n(HE)2 + H]+, [(HE)n+1 + H]+ and [(HE)nC2H4OH]+ (n = 0-2), of which [(HE)1-2 + H]+ dominates. Due to the protic nature of HEHN, cluster fragmentation can be rationalized by proton transfer-mediated elimination of HNO3, HE and HE·HNO3, and the latter two become dominant in larger clusters. [(HE)2 + H]+ and [(HE)nC2H4OH]+ contain H-bonded water and consequently are featured by water elimination in fragmentation. These findings help to evaluate ion formation and fragmentation efficiencies and their impacts on electrospray propulsion.

Ultrasonic Clusterization Process to Prepare [(NNCO)6Co4Cl2] as a Novel Double-Open-Co4O6 Cubane Cluster: SXRD Interactions, DFT, Physicochemical, Thermal Behaviors, and Biomimicking of Catecholase Activity.

A novel double-open-cubane (NNCO)6Co4Cl2 cluster with a Co4O6 core was made available under aqua-ultrasonic open atmosphere conditions for the first time. The ultrasonic clusterization of the (3,5-dimethyl-1H-pyrazol-1-yl)methanol (NNCOH) ligand with CoCl2·6H2O salts in ethanol yielded a high-purity and high-yield cluster product. Energy-dispersive X-ray (EDX), Fourier transform infrared (FT-IR), and ultraviolet (UV)–visible techniques were used to elucidate the clusterization process. The double-open-Co4O6 cubane structure of the (NNCO)6Co4Cl2 cluster was solved by synchrotron single-crystal X-ray diffraction (SXRD) and supported by density functional theory (DFT) optimization and thermogravimetric/differential TG (TG/DTG) measurements; moreover, the DFT structural parameters correlated with the ones determined by SXRD. Molecular electrostatic potential (MEP), Mulliken atomic charge/natural population analysis (MAC/NPA), highest occupied molecular orbital/lowest unoccupied molecular orbital (HOMO/LUMO), density of states (DOS), and GRD quantum analyses were computed at the DFT/B3LYP/6-311G(d,p) theory level. The thermal behavior of the cluster was characterized to support the formation of the Co4O6 core as a stable final product. The catalytic property of the (NNCO)6Co4Cl2 cluster was predestined for the oxidation process of 3,5-DTBC diol (3,5-di-tert-butylbenzene-1,2-diol) to 3,5-DTBQ dione (3,5-di-tert-butylcyclohexa-3,5-diene-1,2-dione).

Synthesis, crystal structure and dft study of benzenesulfonamide compounds 1-ethyl-4-(phenylsulfonyl)piperazine and 1-((3-bromophenyi)sulfonyl)-4-methyipiperazine

Benzenesulfonamide compounds containing piperazine heterocycles have prospective pharmacological activity. Two benzenesulfonamide compounds were synthesized. Based on density functional theory (DFT), the optimized structures of the two molecules were calculated using the B3LYP method. DFT was also used for further study of the molecular electrostatic potential and leading molecular orbital. Single crystal of 1-ethyl-4-(phenylsulfonyl)piperazine was studied. The intermolecular interaction and contact were quantified using Hirshfeld surface analysis and 2D fingerprint plots. Conformational analysis showed that the molecular structure after DFT optimization was consistent with the crystal structure determined by x-ray single crystal diffraction.

Syntheses, crystal structures, theoretical studies, and anticancer properties of an unsymmetrical schiff base ligand N-2-(6-methylpyridyl)-2-hydroxy-1-naphthaldimine and its Ni(II) complex

In this work, syntheses of Schiff-base ligand, N-2-(6-methylpyridyl)-2-hydroxy-1-naphthaldimine (1) and its hitherto unknown chelate with a Ni(II) salt, bis[N-2-(6-methylpyridyl)-2-oxo-1-naphthaldimnato-k3N^N^O] nickel(II) (2) have been reported and characterized by spectral techniques (IR, 1H NMR, Mass). Solid state structures and non-covalent interactions persisting in 1 and 2 are studied by Density Functional Theory (DFT) optimizations and Hirshfeld Surface (HS) analysis. X-ray diffraction (XRD) study shows zwitter ionic keto-amine tautomer form of planar Schiff base 1 that exists as dimer formed by C13 ̶ H13A···O1i [(i) -x+1, -y, -z+1] hydrogen bonds and distorted octahedral geometry around Ni+2 center in chelate 2, where overall crystal structure stability may be attributed to weak C ̶ H···π, π···π stacking, van der Waals interactions, and C ̶ H···O type intermolecular hydrogen bonds. The HS study and 2D Finger Print (FP) plots corroborate well with XRD data and show prominent O···H/H···O spikes (2.2 Å < de+di < 2.3 Å) and C···H/H···C (2.5 Å < de+di < 2.6 Å) spikes that arise from C ̶ H···O type H-bonds and C ̶ H···π interactions respectively, along with significant C···C interactions (de+di ∼ 3.3 Å) due to π···π stacking (2). The anticancer activity has been investigated by using cytotoxicity measure (MTT assay), apoptosis assay, quantitative polymerase chain reaction (qPCR), and colony formation assays. The Ni(II) metal complex demonstrates dose-dependent cytotoxicity in vitro, killing A549 lung cancer cells via an apoptotic pathway.

Synthesis, crystal structure investigation and computational approach to discover potential hydrazide derivatives as a potent inhibitor of cyclooxygenase-2 enzyme.

This study reports the synthesis of two new hydrazide derivatives, namely, (E)-N'-(4- bromobenzylidene)-2-(4-isobutylphenyl)propanehydrazide (4a) and (E)-N'-benzylidene-2-(4-isobutylphenyl)propanehydrazide (4b), respectively. The compounds were synthesized by the reaction of benzaldehyde with Ibuprofen acid hydrazide. Their structures were confirmed by X-ray crystallography. To try to do a more detailed investigation, computational studies including Hirshfeld surface analyses, energy frameworks, density functional theory (DFT)optimizations, frontier orbital analyses, molecular electrostatic potential analyses, and natural bond orbital analyses of the studied compounds are performed. Moreover, molecular docking and dynamics simulations of complexes of the compounds with the cyclooxygenase-2 (COX-2) enzyme were performed to determine the anti-inflammatory potential of the compounds. These analyses predicted the compounds to show maximum chemical interactions and be dynamically stable during simulation time. Furthermore, estimation of binding free energies confirmed the high binding affinity of the compounds for the COX-2 enzyme.

Exploring chemistry features of favipiravir in octanol/water solutions

Favipiravir (Fav) has become a well-known drug for medication of patients by appearance of COVID-19. Heterocyclic structure and connected peptide group could make changes for Fav yielding different features from those required features. Therefore, it is indeed a challenging task to prepare a Fav compound with specific features of desired function. In this work, existence of eight Fav structures by tautomeric formations and peptide group rotations were obtained using density functional theory (DFT) optimization calculations. Gas phase, octanol solution, and water solution were employed to show impact of solution on features of Fav besides obtaining partition coefficients (LogP) for Fav compounds. Significant impacts of solutions were seen on features of Fav with the obtained LogP order: Fav-7 &gt; Fav-8 &gt; Fav-4 &gt; Fav-3 &gt; Fav-2 &gt; Fav-5 &gt; Fav-1 &gt; Fav-6. As a consequence, internal changes yielded significant impacts on features of Fav affirming its carful medication of COVID-19 patients.

Geometrical Structures and Dissociation Channels of CuP2n + (n = 2-11): Studied by Mass Spectrometry and Theoretical Calculations.

Transition metal phosphorus cluster cations CuP2n+ (2 ≤ n ≤ 11) were studied by laser ablation mass spectrometry and collision-induced dissociation (CID). The magic-numbered cluster ion of CuP8+ was identified experimentally, and cluster ions of CuP14+ and CuP18+ were also found to be generated with high abundance. CID results show that the dissociation channels of CuP2n+ (n = 4 and 6–10) are all characterized by the loss of the P4 unit. Theoretical calculations combining global minima searching with the basin-hopping method and density functional theory (DFT) optimizations were performed for these clusters. Among them, the magic-numbered cluster CuP8+ was characterized by a D2d symmetry, with the Cu atom bridging two P4 units. The most stable isomer of CuP14+ was found to be characterized by a C2v symmetry. Calculations also reflect that the dissociation channels of the loss of the P4 unit are more energetically favorable than those of the loss of the P2 unit for CuP2n+ (n = 4 and 6–10), which are in good consistent with the experimental results.

Accelerating the prediction of large carbon clusters via structure search: Evaluation of machine-learning and classical potentials

From as small as single carbon dimers up to giant fullerenes or amorphous nanometer-sized particles, the large family of carbon nanoclusters holds a complex structural variability that increases with cluster size. Capturing this variability and predicting stable allotropes remains a challenging modelling task, crucial to advance technological applications of these materials. While small cluster sizes are traditionally investigated with first-principles methods, a comprehensive study spanning larger sizes calls for a computationally efficient alternative. Here, we combine the stochastic ab initio random structure search algorithm (AIRSS) with geometry optimisations based on interatomic potentials to systematically predict the structure of carbon clusters spanning a wide range of sizes. We first test the transferability and predictive capability of seven widely used carbon potentials, including classical and machine-learning potentials. Results are compared against an analogous cluster dataset generated via AIRSS combined with density functional theory optimizations. The best performing potential, GAP-20, is then employed to predict larger clusters in the nanometer scale, overcoming the computational limits of first-principles approaches. Our complete cluster dataset describes the evolution of topological properties with cluster size, capturing the complex variability of the carbon cluster family. As such, the dataset includes ordered and disordered structures, reproducing well-known clusters, like fullerenes, and predicting novel isomers.